{
 "cells": [
  {
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-14T07:45:14.654958Z",
     "start_time": "2025-01-14T07:45:14.652560Z"
    }
   },
   "outputs": [],
   "execution_count": 45
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-14T07:45:14.682238Z",
     "start_time": "2025-01-14T07:45:14.676964Z"
    }
   },
   "cell_type": "code",
   "source": [
    "df = pd.DataFrame(np.random.randn(5, 4) - 1)\n",
    "print(df)\n",
    "print(np.abs(df))"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          0         1         2         3\n",
      "0  1.978133 -0.787264 -0.545305 -0.853179\n",
      "1 -0.281145 -0.357790 -2.222071 -2.109984\n",
      "2  0.003620 -0.028458 -1.032956 -0.418886\n",
      "3  0.182940 -0.252549 -1.957989 -1.012583\n",
      "4 -0.759632 -1.091981 -0.840034 -2.345289\n",
      "          0         1         2         3\n",
      "0  1.978133  0.787264  0.545305  0.853179\n",
      "1  0.281145  0.357790  2.222071  2.109984\n",
      "2  0.003620  0.028458  1.032956  0.418886\n",
      "3  0.182940  0.252549  1.957989  1.012583\n",
      "4  0.759632  1.091981  0.840034  2.345289\n"
     ]
    }
   ],
   "execution_count": 46
  },
  {
   "cell_type": "code",
   "source": "print(df.apply(lambda x: x.max()))",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-14T07:45:14.737593Z",
     "start_time": "2025-01-14T07:45:14.734246Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1.978133\n",
      "1   -0.028458\n",
      "2   -0.545305\n",
      "3   -0.418886\n",
      "dtype: float64\n"
     ]
    }
   ],
   "execution_count": 47
  },
  {
   "cell_type": "code",
   "source": "print(df.apply(lambda x: x.max(), axis=1))",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-14T07:45:14.758350Z",
     "start_time": "2025-01-14T07:45:14.754598Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    1.978133\n",
      "1   -0.281145\n",
      "2    0.003620\n",
      "3    0.182940\n",
      "4   -0.759632\n",
      "dtype: float64\n"
     ]
    }
   ],
   "execution_count": 48
  },
  {
   "cell_type": "code",
   "source": [
    "print(df.map(lambda x: '%.2f' % x))\n",
    "df.dtypes"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-14T07:45:14.810967Z",
     "start_time": "2025-01-14T07:45:14.805362Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       0      1      2      3\n",
      "0   1.98  -0.79  -0.55  -0.85\n",
      "1  -0.28  -0.36  -2.22  -2.11\n",
      "2   0.00  -0.03  -1.03  -0.42\n",
      "3   0.18  -0.25  -1.96  -1.01\n",
      "4  -0.76  -1.09  -0.84  -2.35\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    float64\n",
       "1    float64\n",
       "2    float64\n",
       "3    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 49
  },
  {
   "cell_type": "code",
   "source": [
    "type('%.2f' % 1.3456)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-14T07:45:14.828850Z",
     "start_time": "2025-01-14T07:45:14.824973Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 50
  },
  {
   "cell_type": "code",
   "source": [
    "print(np.random.randint(5, size=5))\n",
    "print('-' * 50)\n",
    "s4 = pd.Series(range(10, 15), index=np.random.randint(5, size=5))\n",
    "print(s4)\n",
    "print('-' * 50)\n",
    "print(s4.sort_index())\n",
    "print(s4)\n",
    "print(s4.iloc[0:3])\n",
    "print(s4[0:3])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-14T07:45:14.856346Z",
     "start_time": "2025-01-14T07:45:14.850856Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 1 3 1 2]\n",
      "--------------------------------------------------\n",
      "4    10\n",
      "3    11\n",
      "3    12\n",
      "1    13\n",
      "1    14\n",
      "dtype: int64\n",
      "--------------------------------------------------\n",
      "1    13\n",
      "1    14\n",
      "3    12\n",
      "3    11\n",
      "4    10\n",
      "dtype: int64\n",
      "4    10\n",
      "3    11\n",
      "3    12\n",
      "1    13\n",
      "1    14\n",
      "dtype: int64\n",
      "4    10\n",
      "3    11\n",
      "3    12\n",
      "dtype: int64\n",
      "4    10\n",
      "3    11\n",
      "3    12\n",
      "dtype: int64\n"
     ]
    }
   ],
   "execution_count": 51
  },
  {
   "cell_type": "code",
   "source": [
    "df4 = pd.DataFrame(np.random.randn(5, 5),\n",
    "                   index=np.random.randint(5, size=5),\n",
    "                   columns=np.random.randint(5, size=5))\n",
    "print(df4)\n",
    "print('-' * 50)\n",
    "df4_isort = df4.sort_index(axis=0, ascending=False)\n",
    "print(df4_isort)\n",
    "print('-' * 50)\n",
    "df4_isort = df4.sort_index(axis=0, ascending=True)\n",
    "print(df4_isort)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-14T07:45:14.894390Z",
     "start_time": "2025-01-14T07:45:14.886356Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          3         3         4         0         4\n",
      "2  0.601200  0.765496  0.273556  0.112567  0.545121\n",
      "3 -0.793114 -0.770779 -1.132091 -0.360510 -0.588195\n",
      "0  0.438097  0.790833  1.000866 -0.407764 -0.029632\n",
      "1  0.502203  0.411760  0.864719 -1.047763  1.549052\n",
      "3  0.429497  0.436641 -1.015443 -0.299262 -0.841597\n",
      "--------------------------------------------------\n",
      "          3         3         4         0         4\n",
      "3 -0.793114 -0.770779 -1.132091 -0.360510 -0.588195\n",
      "3  0.429497  0.436641 -1.015443 -0.299262 -0.841597\n",
      "2  0.601200  0.765496  0.273556  0.112567  0.545121\n",
      "1  0.502203  0.411760  0.864719 -1.047763  1.549052\n",
      "0  0.438097  0.790833  1.000866 -0.407764 -0.029632\n",
      "--------------------------------------------------\n",
      "          3         3         4         0         4\n",
      "0  0.438097  0.790833  1.000866 -0.407764 -0.029632\n",
      "1  0.502203  0.411760  0.864719 -1.047763  1.549052\n",
      "2  0.601200  0.765496  0.273556  0.112567  0.545121\n",
      "3 -0.793114 -0.770779 -1.132091 -0.360510 -0.588195\n",
      "3  0.429497  0.436641 -1.015443 -0.299262 -0.841597\n"
     ]
    }
   ],
   "execution_count": 52
  },
  {
   "cell_type": "code",
   "source": [
    "df4_isort = df4.sort_index(axis=1, ascending=True)\n",
    "print(df4_isort)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T07:45:14.911652Z",
     "start_time": "2025-01-14T07:45:14.907387Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          0         3         3         4         4\n",
      "2  0.112567  0.601200  0.765496  0.273556  0.545121\n",
      "3 -0.360510 -0.793114 -0.770779 -1.132091 -0.588195\n",
      "0 -0.407764  0.438097  0.790833  1.000866 -0.029632\n",
      "1 -1.047763  0.502203  0.411760  0.864719  1.549052\n",
      "3 -0.299262  0.429497  0.436641 -1.015443 -0.841597\n"
     ]
    }
   ],
   "execution_count": 53
  },
  {
   "cell_type": "code",
   "source": [
    "import random\n",
    "\n",
    "l = [random.randint(0, 100) for i in range(24)]\n",
    "df4 = pd.DataFrame(np.array(l).reshape(6, 4))\n",
    "print(df4)\n",
    "print('-' * 50)\n",
    "df4_vsort = df4.sort_values(by=3, axis=0, ascending=False)\n",
    "print(df4_vsort)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-14T07:45:14.959626Z",
     "start_time": "2025-01-14T07:45:14.954663Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    0   1    2   3\n",
      "0  68  80   38  58\n",
      "1  75   9   55  47\n",
      "2  85  95  100  90\n",
      "3  18  62   23  14\n",
      "4  47  57   13   0\n",
      "5  77  22    0   7\n",
      "--------------------------------------------------\n",
      "    0   1    2   3\n",
      "2  85  95  100  90\n",
      "0  68  80   38  58\n",
      "1  75   9   55  47\n",
      "3  18  62   23  14\n",
      "5  77  22    0   7\n",
      "4  47  57   13   0\n"
     ]
    }
   ],
   "execution_count": 54
  },
  {
   "cell_type": "code",
   "source": [
    "df4_vsort = df4.sort_values(by=3, axis=1, ascending=False)\n",
    "print(df4_vsort)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T07:45:14.977617Z",
     "start_time": "2025-01-14T07:45:14.973630Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    1    2   0   3\n",
      "0  80   38  68  58\n",
      "1   9   55  75  47\n",
      "2  95  100  85  90\n",
      "3  62   23  18  14\n",
      "4  57   13  47   0\n",
      "5  22    0  77   7\n"
     ]
    }
   ],
   "execution_count": 55
  },
  {
   "cell_type": "code",
   "source": [
    "df_data = pd.DataFrame([np.random.randn(3), [1., 2., np.nan],\n",
    "                        [np.nan, 4., np.nan], [1., 2., 3.]])\n",
    "print(df_data.head())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-14T07:45:14.982441Z",
     "start_time": "2025-01-14T07:45:14.978619Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          0         1        2\n",
      "0  0.195542 -0.463144  0.72753\n",
      "1  1.000000  2.000000      NaN\n",
      "2       NaN  4.000000      NaN\n",
      "3  1.000000  2.000000  3.00000\n"
     ]
    }
   ],
   "execution_count": 56
  },
  {
   "cell_type": "code",
   "source": "df_data.iloc[2, 0]",
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T07:45:15.014496Z",
     "start_time": "2025-01-14T07:45:15.010445Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(nan)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 57
  },
  {
   "cell_type": "code",
   "source": "print(df_data.isnull())",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-14T07:45:15.035665Z",
     "start_time": "2025-01-14T07:45:15.031501Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       0      1      2\n",
      "0  False  False  False\n",
      "1  False  False   True\n",
      "2   True  False   True\n",
      "3  False  False  False\n"
     ]
    }
   ],
   "execution_count": 58
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-14T07:45:15.069050Z",
     "start_time": "2025-01-14T07:45:15.064673Z"
    }
   },
   "cell_type": "code",
   "source": "print(df_data.isnull().sum() / len(df_data))",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.25\n",
      "1    0.00\n",
      "2    0.50\n",
      "dtype: float64\n"
     ]
    }
   ],
   "execution_count": 59
  },
  {
   "cell_type": "code",
   "source": "print(df_data.dropna(subset=[0]))",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-14T07:45:15.074539Z",
     "start_time": "2025-01-14T07:45:15.070051Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          0         1        2\n",
      "0  0.195542 -0.463144  0.72753\n",
      "1  1.000000  2.000000      NaN\n",
      "3  1.000000  2.000000  3.00000\n"
     ]
    }
   ],
   "execution_count": 60
  },
  {
   "cell_type": "code",
   "source": [
    "df_data"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T07:45:15.091836Z",
     "start_time": "2025-01-14T07:45:15.086544Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "          0         1        2\n",
       "0  0.195542 -0.463144  0.72753\n",
       "1  1.000000  2.000000      NaN\n",
       "2       NaN  4.000000      NaN\n",
       "3  1.000000  2.000000  3.00000"
      ],
      "text/html": [
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     "execution_count": 61,
     "metadata": {},
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   ],
   "execution_count": 61
  },
  {
   "cell_type": "code",
   "source": "print(df_data.dropna(axis=1))",
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-14T07:45:15.126142Z",
     "start_time": "2025-01-14T07:45:15.121839Z"
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    {
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     "output_type": "stream",
     "text": [
      "          1\n",
      "0 -0.463144\n",
      "1  2.000000\n",
      "2  4.000000\n",
      "3  2.000000\n"
     ]
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   "execution_count": 62
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   "cell_type": "code",
   "source": [
    "df_data"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-01-14T07:45:15.148731Z",
     "start_time": "2025-01-14T07:45:15.144148Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "          0         1        2\n",
       "0  0.195542 -0.463144  0.72753\n",
       "1  1.000000  2.000000      NaN\n",
       "2       NaN  4.000000      NaN\n",
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       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>4.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 63
  },
  {
   "cell_type": "code",
   "source": [
    "print(df_data.iloc[:, 0].fillna(-100.))\n",
    "print('-' * 50)\n",
    "print(df_data)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2025-01-14T07:45:15.189160Z",
     "start_time": "2025-01-14T07:45:15.184740Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      0.195542\n",
      "1      1.000000\n",
      "2   -100.000000\n",
      "3      1.000000\n",
      "Name: 0, dtype: float64\n",
      "--------------------------------------------------\n",
      "          0         1        2\n",
      "0  0.195542 -0.463144  0.72753\n",
      "1  1.000000  2.000000      NaN\n",
      "2       NaN  4.000000      NaN\n",
      "3  1.000000  2.000000  3.00000\n"
     ]
    }
   ],
   "execution_count": 64
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-14T07:45:15.213867Z",
     "start_time": "2025-01-14T07:45:15.207161Z"
    }
   },
   "cell_type": "code",
   "source": [
    "df_data.iloc[:, 2] = df_data.iloc[:, 2].fillna(df_data.iloc[:, 2].mean())\n",
    "df_data"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "          0         1         2\n",
       "0  0.195542 -0.463144  0.727530\n",
       "1  1.000000  2.000000  1.863765\n",
       "2       NaN  4.000000  1.863765\n",
       "3  1.000000  2.000000  3.000000"
      ],
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     },
     "execution_count": 65,
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   "execution_count": 65
  },
  {
   "cell_type": "code",
   "source": [
    "for i in df_data.columns:\n",
    "    print(df_data.loc[:, i])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
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     "start_time": "2025-01-14T07:45:47.749922Z"
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      "2    1.863765\n",
      "3    3.000000\n",
      "Name: 2, dtype: float64\n"
     ]
    }
   ],
   "execution_count": 70
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